marketing

Seth Godin just gave a riveting talk at the Global Health and Innovation Conference. You may have read some of his books, like Purple Cow and Tribes. Inspired by his talk, I applied some of Seth's key points to innovation in communicating health data. If you have data that you want used more broadly, here are some basic thoughts to guide you:

Target specific audiences first. A data product cannot appeal to all audiences at once. There is a shrinking "middle of the market" as people get used to having their specific information needs addressed by tailored products. So start with presenting your data in ways that appeal to those small audiences that are most likely to embrace and use your data.

Make it easy to talk about your data. People have to be able to discuss your data easily to be able to talk about and recommend them to others. It is important to give potential users a sense that "people like me use these data". Ultimately, word of mouth will lead to broader adoption.

Make your data product "remarkable" (i.e. worth making remarks about). This is Seth Godin's "purple cow". If people see a cow, it's not remarkable. If they were to see a purple cow, they would talk about it (or about Milka, but that's a different story). For health data, that purple cow could be a new form of visualization, a surprising story, a provocative infographic, or a useful smartphone app that people will talk about.

Go out on a limb. If you are trying to innovate, you can't avoid failure. If "failure is not an option", you are not innovating. Fail fast and iterate quickly, until you get it right.

To get more inspiration, follow Seth's blog or subscribe to this blog (feed is here); there will be follow-ups to this post in the near future.

In a fascinating article, How Companies learn your secrets, New York Times reporter Charles Duhigg provides a great case study on how Target uses sophisticated analysis to identify pregnant women and target them (pun intended) with well-timed coupons and ads. He goes on to describe the Science of Habit Formation and the interaction of cue, routine and reward (also the subject of Duhigg's upcoming book, The Power of Habit).

I was particularly intrigued by Duhigg's description of the analytics activities in Target's marketing effort. Their analysis is based on very rich and deep data on their customers. Target marketers know that the best opportunity to get consumers to change purchasing habits is at life-changing events like weddings, moves, divorces, and particularly with the arrival of a baby. So they focused their activities on identifying pregnant women. Quoting from the article:

For decades, Target has collected vast amounts of data on every person who regularly walks into one of its stores. Whenever possible, Target assigns each shopper a unique code — known internally as the Guest ID number — that keeps tabs on everything they buy. “If you use a credit card or a coupon, or ﬁll out a survey, or mail in a refund, or call the customer help line, or open an e-mail we’ve sent you or visit our Web site, we’ll record it and link it to your Guest ID,” Pole said. “We want to know everything we can.”

Also linked to your Guest ID is demographic information like your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you’ve moved recently, what credit cards you carry in your wallet and what Web sites you visit. Target can buy data about your ethnicity, job history, the magazines you read, if you’ve ever declared bankruptcy or got divorced, the year you bought (or lost) your house, where you went to college, what kinds of topics you talk about online, whether you prefer certain brands of coffee, paper towels, cereal or applesauce, your political leanings, reading habits, charitable giving and the number of cars you own.

Quite the arsenal of data. Target’s Guest Marketing Analytics department then crunches the data to develop insights that help target marketing and advertising campaigns. Target analysts managed to develop a pregnancy prediction score based on a customer's purchasing history of 25 products; they also manage to estimate a woman's due date "within a small window" and can send women advertising that aligns very well with the stage of their pregnancy.

The targeting can be quite successful as an example in the article illustrates. The example fits so well it almost seems to be made up:

About a year after [Target] created [their] pregnancy-prediction model, a man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation.

“My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”

The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again.

On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”

What does that have to do with Health Data Innovation? "The best minds of my generation are thinking about how to make people click ads," says Jeff Hammerbacher, co-founder and Chief Scientist at Cloudera. "That sucks." This quote also applies here. There is a lot of money to be made in understanding consumer behavior. But there is a lot of health gain to be made by putting the same energy into improving people's health by enhancing prevention, identifying early warning signs for diseases, better understanding effectiveness and side-effects of drugs and devices, and treat specific combinations of diseases. Instead of analyzing people's purchasing records, browsing history and reaction to different types of discounts, the smartest minds of our generation should analyze health records, personal history and reaction to different types of treatment.

A lot of health data are being collected on each individual. There are health records at general practitioners, outpatient facilities, hospitals, and emergency services; pharmacy records; vital registration records; health insurance data; responses to surveys, census, and other data collection efforts; participation in clinical trials; and many others. In addition, data used for marketing analytics can also be used for identifying health issues, including purchasing and browsing history, social media data, and many of the other data points listed in Target's data arsenal above. There are many triggers that could suggest that an individual should consult a physician, e.g. if people start buying unusual quantities of over-the-counter drugs, research specific symptoms online, tweet about sudden weight loss (or gain), or changing reading habits. Putting all these data sources together could provide a much deeper picture of someone's health than any provider's record.

Most consumers are appalled when they learn how much data retailers and other organizations amass on them. Compiling health data should happen with the permission of the patient. Some health care providers have started to collect broader data about their patients and use those data for prevention and more effective treatment. Kaiser Permanente has been a front runner in the quest to better use data for the benefit of the patient; they call it "collecting information for personalized high quality care". They just launched an Android App to make it easier for patients to access and submit information about their health.

There are many ways to improve how we currently deliver health care. Having more of the best minds of our generation working on data to improve health instead of improving ad responses and click rates would certainly help. Instead of a retailer knowing that a girl is pregnant (before her dad does), a physician should know that his patient is increasingly likely to have a heart attack (before he has one).